CONSORT flow

consort <- readRDS(here("data", "derived_data", "completion_summary.rds")) %>% 
  spread(reason, n) %>% 
  mutate(group = 1:n(),
         assigned = Approved + `Failed first attention check`,
         issue = str_replace_all(issue, "assistance", "assist.")) %>% 
  mutate(assigned_label = glue("Allocated to Group {group}\n{crackdown}\n{issue}\n{funding} funding\n\nN = {assigned}"),
         completed_label = glue("Completed\nN = {Approved}\n\n{`Failed first attention check`} failed\nattention check"))

assessed_eligibility_n <- sum(consort$Approved, consort$`Failed first attention check`, 
                              consort$`Took survey outisde of MTurk`)
ineligible_n <- sum(consort$`Took survey outisde of MTurk`)
randomized_n <- sum(consort$Approved, consort$`Failed first attention check`)


# https://aghaynes.wordpress.com/2018/05/09/flow-charts-in-r/
# set some parameters to use repeatedly
width <- 0.1
xs <- seq(0.1, 0.9, length.out = 8)
allocated_y <- 0.375
completed_y <- 0.125

box_gp_grey <- gpar(fill = ngo_cols("light grey"))
box_gp_blue_dk <- gpar(fill = ngo_cols("blue"), alpha = 0.75)
box_gp_blue_lt <- gpar(fill = ngo_cols("blue"), alpha = 0.35)
box_gp_green <- gpar(fill = ngo_cols("green"), alpha = 0.65)
box_gp_yellow <- gpar(fill = ngo_cols("yellow"))
box_gp_orange <- gpar(fill = ngo_cols("orange"), alpha = 0.65)

txt_gp <- gpar(fontfamily = "Roboto Condensed", 
               fontface = "plain", fontsize = 8)

# Create boxes
total <- boxGrob(glue("Assessed for eligibility\n N = {assessed_eligibility_n}"), 
                 x = 0.5, y = 0.9, width = 2 * width,
                 box_gp = box_gp_blue_lt, txt_gp = txt_gp)
randomized <- boxGrob(glue("Randomized\n N = {randomized_n}"), 
                      x = 0.5, y = 0.65, width = 2 * width,
                      box_gp = box_gp_blue_dk, txt_gp = txt_gp)
ineligible <- boxGrob(glue("Participants excluded for\ncompleting Qualtrics survey\noutside of MTurk\n N = {ineligible_n}"), 
                      x = xs[7], y = 0.775, #width = 0.25,
                      box_gp = box_gp_yellow, txt_gp = txt_gp)

group1 <- boxGrob(filter(consort, group == 1)$assigned_label,
                  x = xs[1], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group2 <- boxGrob(filter(consort, group == 2)$assigned_label,
                  x = xs[2], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group3 <- boxGrob(filter(consort, group == 3)$assigned_label,
                  x = xs[3], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group4 <- boxGrob(filter(consort, group == 4)$assigned_label,
                  x = xs[4], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group5 <- boxGrob(filter(consort, group == 5)$assigned_label,
                  x = xs[5], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group6 <- boxGrob(filter(consort, group == 6)$assigned_label,
                  x = xs[6], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group7 <- boxGrob(filter(consort, group == 7)$assigned_label,
                  x = xs[7], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group8 <- boxGrob(filter(consort, group == 8)$assigned_label,
                  x = xs[8], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)

group1_completed <- boxGrob(filter(consort, group == 1)$completed_label, 
                            x = xs[1], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group2_completed <- boxGrob(filter(consort, group == 2)$completed_label, 
                            x = xs[2], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group3_completed <- boxGrob(filter(consort, group == 3)$completed_label, 
                            x = xs[3], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group4_completed <- boxGrob(filter(consort, group == 4)$completed_label, 
                            x = xs[4], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group5_completed <- boxGrob(filter(consort, group == 5)$completed_label, 
                            x = xs[5], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group6_completed <- boxGrob(filter(consort, group == 6)$completed_label, 
                            x = xs[6], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group7_completed <- boxGrob(filter(consort, group == 7)$completed_label, 
                            x = xs[7], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group8_completed <- boxGrob(filter(consort, group == 8)$completed_label, 
                            x = xs[8], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)

total_random_connect <- connectGrob(total, randomized, "v")
total_ineligible_connect <- connectGrob(total, ineligible, "-")

rand_connect1 <- connectGrob(randomized, group1, "N")
rand_connect2 <- connectGrob(randomized, group2, "N")
rand_connect3 <- connectGrob(randomized, group3, "N")
rand_connect4 <- connectGrob(randomized, group4, "N")
rand_connect5 <- connectGrob(randomized, group5, "N")
rand_connect6 <- connectGrob(randomized, group6, "N")
rand_connect7 <- connectGrob(randomized, group7, "N")
rand_connect8 <- connectGrob(randomized, group8, "N")

complete_connect1 <- connectGrob(group1, group1_completed, "N")
complete_connect2 <- connectGrob(group2, group2_completed, "N")
complete_connect3 <- connectGrob(group3, group3_completed, "N")
complete_connect4 <- connectGrob(group4, group4_completed, "N")
complete_connect5 <- connectGrob(group5, group5_completed, "N")
complete_connect6 <- connectGrob(group6, group6_completed, "N")
complete_connect7 <- connectGrob(group7, group7_completed, "N")
complete_connect8 <- connectGrob(group8, group8_completed, "N")

full_chart <- list(total, randomized, ineligible, total_random_connect, total_ineligible_connect,
                   group1, group2, group3, group4, group5, group6, group7, group8,
                   rand_connect1, rand_connect2, rand_connect3, rand_connect4, 
                   rand_connect5, rand_connect6, rand_connect7, rand_connect8,
                   group1_completed, group2_completed, group3_completed, group4_completed, 
                   group5_completed, group6_completed, group7_completed, group8_completed,
                   complete_connect1, complete_connect2, complete_connect3, complete_connect4, 
                   complete_connect5, complete_connect6, complete_connect7, complete_connect8) 

Characteristics of experiment samples

We compare our sample with demographic characteristics of the general population. Since there is no nationally representative sample for each of our demographic variables, we use two waves of the US Census’s Current Population Survey (CPS), with data from the Minnesota Population Center’s Integrated Public Use Microdata Series (IPUMS).

For general demographic information, we use data from the 2017 Annual Social and Economic Supplement (ASEC) for the CPS. From 2002–2015, the CPS included a Volunteer Supplement every September, so we use 2015 data for data on volunteering and donating to charity.

IPUMS requires that you manually generate a data extract through their website, so downloading data from them is not entirely automated or reproducible. We created two extracts (though this could have been combined into one), with the following variables

  • "data/raw_data/ipums-cps/cps_2017.dat.gz": 2017 ASEC, with the following variables selected (in addition to whatever IPUMS preselects by default) (and weighted by ASECWT):
    • AGE
    • SEX
    • EDUC
    • INCTOT
  • "data/raw_data/ipums-cps/cps_09_2015.dat.gz": September 2015 basic monthly CPS (which includes the Volunteer Supplement), with the following variables selected (and weighted by VLSUPPWT):
    • VLSTATUS
    • VLDONATE

We do not show other respondent demographic details because we don’t have good population-level data to compare our sample with. We could theoretically use Pew data for political preferences, but they collect data on party affiliation, while we collected data about respondent positions along a conservative–liberal spectrum, so the two variables aren’t comparable.

Characteristics of experimental sample {#tbl:exp-sample}
Variable Sample National median 95% HPDI
Female (%)a 54.8% 51.0% 3.8% (-0.4%, 8%)
Age (% 35+)a 47.3% 53.9% -6.6% (-10.8%, -2.4%)
Income (% $50,000+)a 50.4% 27.4% 21.7% (17.4%, 25.8%)
Education (% BA+)a 46.1% 29.9% 16.3% (11.9%, 20.2%)
Donated in past year (%)b 82.5% 48.8% 33.6% (30.2%, 36.7%)
Volunteered in past year (%)b 54.2% 75.1% -20.9% (-25%, -16.6%)
aAnnual CPS, March 2017
bMonthly CPS, September 2015
National value is outside the sample highest posterior density interval (HPDI)

Original computing environment

## # http://dirk.eddelbuettel.com/blog/2017/11/27/#011_faster_package_installation_one
## VER=
## CCACHE=ccache
## CC=$(CCACHE) gcc$(VER)
## CXX=$(CCACHE) g++$(VER)
## CXXFLAGS=-O3 -Wno-unused-variable -Wno-unused-function -Wno-unused-local-typedefs
## CXX11=$(CCACHE) g++$(VER)
## CXX14=$(CCACHE) g++$(VER)
## FLIBS = -L`gfortran -print-file-name=libgfortran.dylib | xargs dirname`
## FC=$(CCACHE) gfortran$(VER)
## F77=$(CCACHE) gfortran$(VER)
## ─ Session info ──────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.5.2 (2018-12-20)
##  os       macOS Mojave 10.14.3        
##  system   x86_64, darwin15.6.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       America/Denver              
##  date     2019-03-13                  
## 
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##  forestplot               1.7.2      2017-09-16 [1]
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##  fs                       1.2.6      2018-08-23 [1]
##  generics                 0.0.2      2018-11-29 [1]
##  ggplot2                * 3.1.0      2018-10-25 [1]
##  glue                   * 1.3.0.9000 2019-02-09 [1]
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##  here                   * 0.1        2017-05-28 [1]
##  hipread                  0.1.1      2018-08-02 [1]
##  Hmisc                    4.1-1      2018-01-03 [1]
##  hms                      0.4.2      2018-03-10 [1]
##  htmlTable              * 1.13.1     2019-01-07 [1]
##  htmltools                0.3.6      2017-04-28 [1]
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## 
## [1] /Library/Frameworks/R.framework/Versions/3.5/Resources/library
---
title: "Additional analysis"
author: "Andrew Heiss and Suparna Chaudhry"
date: "Last run: `r format(Sys.time(), '%B %e, %Y')`"
output: 
  html_document:
    code_folding: hide
editor_options: 
  chunk_output_type: console
---

```{r load-libraries-data, warning=FALSE, message=FALSE}
# Load libraries
library(tidyverse)
library(crackdownsphilanthropy)
library(magrittr)
library(rstan)
library(broom)
library(glue)
library(grid)
library(gridExtra)
library(Gmisc)
library(pander)
library(scales)
library(ipumsr)
library(here)

source(here("analysis", "options.R"))

# Load data
results <- readRDS(here("data", "derived_data", "results_clean.rds"))
```

# CONSORT flow

```{r build-consort}
consort <- readRDS(here("data", "derived_data", "completion_summary.rds")) %>% 
  spread(reason, n) %>% 
  mutate(group = 1:n(),
         assigned = Approved + `Failed first attention check`,
         issue = str_replace_all(issue, "assistance", "assist.")) %>% 
  mutate(assigned_label = glue("Allocated to Group {group}\n{crackdown}\n{issue}\n{funding} funding\n\nN = {assigned}"),
         completed_label = glue("Completed\nN = {Approved}\n\n{`Failed first attention check`} failed\nattention check"))

assessed_eligibility_n <- sum(consort$Approved, consort$`Failed first attention check`, 
                              consort$`Took survey outisde of MTurk`)
ineligible_n <- sum(consort$`Took survey outisde of MTurk`)
randomized_n <- sum(consort$Approved, consort$`Failed first attention check`)


# https://aghaynes.wordpress.com/2018/05/09/flow-charts-in-r/
# set some parameters to use repeatedly
width <- 0.1
xs <- seq(0.1, 0.9, length.out = 8)
allocated_y <- 0.375
completed_y <- 0.125

box_gp_grey <- gpar(fill = ngo_cols("light grey"))
box_gp_blue_dk <- gpar(fill = ngo_cols("blue"), alpha = 0.75)
box_gp_blue_lt <- gpar(fill = ngo_cols("blue"), alpha = 0.35)
box_gp_green <- gpar(fill = ngo_cols("green"), alpha = 0.65)
box_gp_yellow <- gpar(fill = ngo_cols("yellow"))
box_gp_orange <- gpar(fill = ngo_cols("orange"), alpha = 0.65)

txt_gp <- gpar(fontfamily = "Roboto Condensed", 
               fontface = "plain", fontsize = 8)

# Create boxes
total <- boxGrob(glue("Assessed for eligibility\n N = {assessed_eligibility_n}"), 
                 x = 0.5, y = 0.9, width = 2 * width,
                 box_gp = box_gp_blue_lt, txt_gp = txt_gp)
randomized <- boxGrob(glue("Randomized\n N = {randomized_n}"), 
                      x = 0.5, y = 0.65, width = 2 * width,
                      box_gp = box_gp_blue_dk, txt_gp = txt_gp)
ineligible <- boxGrob(glue("Participants excluded for\ncompleting Qualtrics survey\noutside of MTurk\n N = {ineligible_n}"), 
                      x = xs[7], y = 0.775, #width = 0.25,
                      box_gp = box_gp_yellow, txt_gp = txt_gp)

group1 <- boxGrob(filter(consort, group == 1)$assigned_label,
                  x = xs[1], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group2 <- boxGrob(filter(consort, group == 2)$assigned_label,
                  x = xs[2], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group3 <- boxGrob(filter(consort, group == 3)$assigned_label,
                  x = xs[3], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group4 <- boxGrob(filter(consort, group == 4)$assigned_label,
                  x = xs[4], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group5 <- boxGrob(filter(consort, group == 5)$assigned_label,
                  x = xs[5], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group6 <- boxGrob(filter(consort, group == 6)$assigned_label,
                  x = xs[6], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group7 <- boxGrob(filter(consort, group == 7)$assigned_label,
                  x = xs[7], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group8 <- boxGrob(filter(consort, group == 8)$assigned_label,
                  x = xs[8], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)

group1_completed <- boxGrob(filter(consort, group == 1)$completed_label, 
                            x = xs[1], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group2_completed <- boxGrob(filter(consort, group == 2)$completed_label, 
                            x = xs[2], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group3_completed <- boxGrob(filter(consort, group == 3)$completed_label, 
                            x = xs[3], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group4_completed <- boxGrob(filter(consort, group == 4)$completed_label, 
                            x = xs[4], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group5_completed <- boxGrob(filter(consort, group == 5)$completed_label, 
                            x = xs[5], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group6_completed <- boxGrob(filter(consort, group == 6)$completed_label, 
                            x = xs[6], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group7_completed <- boxGrob(filter(consort, group == 7)$completed_label, 
                            x = xs[7], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group8_completed <- boxGrob(filter(consort, group == 8)$completed_label, 
                            x = xs[8], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)

total_random_connect <- connectGrob(total, randomized, "v")
total_ineligible_connect <- connectGrob(total, ineligible, "-")

rand_connect1 <- connectGrob(randomized, group1, "N")
rand_connect2 <- connectGrob(randomized, group2, "N")
rand_connect3 <- connectGrob(randomized, group3, "N")
rand_connect4 <- connectGrob(randomized, group4, "N")
rand_connect5 <- connectGrob(randomized, group5, "N")
rand_connect6 <- connectGrob(randomized, group6, "N")
rand_connect7 <- connectGrob(randomized, group7, "N")
rand_connect8 <- connectGrob(randomized, group8, "N")

complete_connect1 <- connectGrob(group1, group1_completed, "N")
complete_connect2 <- connectGrob(group2, group2_completed, "N")
complete_connect3 <- connectGrob(group3, group3_completed, "N")
complete_connect4 <- connectGrob(group4, group4_completed, "N")
complete_connect5 <- connectGrob(group5, group5_completed, "N")
complete_connect6 <- connectGrob(group6, group6_completed, "N")
complete_connect7 <- connectGrob(group7, group7_completed, "N")
complete_connect8 <- connectGrob(group8, group8_completed, "N")

full_chart <- list(total, randomized, ineligible, total_random_connect, total_ineligible_connect,
                   group1, group2, group3, group4, group5, group6, group7, group8,
                   rand_connect1, rand_connect2, rand_connect3, rand_connect4, 
                   rand_connect5, rand_connect6, rand_connect7, rand_connect8,
                   group1_completed, group2_completed, group3_completed, group4_completed, 
                   group5_completed, group6_completed, group7_completed, group8_completed,
                   complete_connect1, complete_connect2, complete_connect3, complete_connect4, 
                   complete_connect5, complete_connect6, complete_connect7, complete_connect8) 
```

```{r show-save-consort, fig.width=10, fig.height=6}
# Ordinarily, you can use grid.grab() to save the output of a grid figure into
# an object and then use that in ggsave(). However, when knitting, this creates
# a duplicate plot, which is frustrating. So instead, we use walk() to reprint
# all the grobs within specific pdf and png devices
#
# See https://stackoverflow.com/a/17509770/120898 for a similar issue

# Save as PDF
cairo_pdf(filename = here("analysis", "output", "figures", "consort.pdf"),
          width = 10, height = 6)
grid.newpage()
walk(full_chart, ~ print(.))
invisible(dev.off())

# Save as PNG
png(filename = here("analysis", "output", "figures", "consort.png"), 
    width = 10, height = 6, units = "in",
    bg = "white", res = 300, type = "cairo")
grid.newpage()
walk(full_chart, ~ print(.))
invisible(dev.off())

# Show in knitted document
grid.newpage()
walk(full_chart, ~ print(.))
```


# Characteristics of experiment samples

We compare our sample with demographic characteristics of the general population. Since there is no nationally representative sample for each of our demographic variables, we use two waves of the US Census's Current Population Survey (CPS), with data from the [Minnesota Population Center's Integrated Public Use Microdata Series (IPUMS)](https://cps.ipums.org/cps/).

For general demographic information, we use data from the 2017 [Annual Social and Economic Supplement (ASEC)](https://cps.ipums.org/cps/asec_sample_notes.shtml) for the CPS. From 2002–2015, the CPS included a [Volunteer Supplement](https://cps.ipums.org/cps/volunteer_sample_notes.shtml) every September, so we use 2015 data for data on volunteering and donating to charity.

IPUMS requires that you manually generate a data extract through their website, so downloading data from them is not entirely automated or reproducible. We created two extracts (though this could have been combined into one), with the following variables

-  `"data/raw_data/ipums-cps/cps_2017.dat.gz"`: 2017 ASEC, with the following variables selected (in addition to whatever IPUMS preselects by default) (and weighted by `ASECWT`):
    - `AGE`
    - `SEX`
    - `EDUC`
    - `INCTOT`
-  `"data/raw_data/ipums-cps/cps_09_2015.dat.gz"`: September 2015 basic monthly CPS (which includes the Volunteer Supplement), with the following variables selected (and weighted by `VLSUPPWT`):
    - `VLSTATUS`
    - `VLDONATE`

We do not show other respondent demographic details because we don't have good population-level data to compare our sample with. We could theoretically use Pew data for political preferences, but they collect data on party affiliation, while we collected data about respondent positions along a conservative–liberal spectrum, so the two variables aren't comparable.

```{r load-clean-cps, warning=FALSE, message=FALSE}
cps_2015_ddi_file <- here("data", "raw_data", "ipums-cps", "cps_09_2015.xml")
cps_2015_data_file <- here("data", "raw_data", "ipums-cps", "cps_09_2015.dat.gz")

cps_2015_ddi <- read_ipums_ddi(cps_2015_ddi_file)
cps_2015_data <- read_ipums_micro(cps_2015_ddi_file, data_file = cps_2015_data_file, verbose = FALSE)

cps_2017_ddi_file <- here("data", "raw_data", "ipums-cps", "cps_2017.xml")
cps_2017_data_file <- here("data", "raw_data", "ipums-cps", "cps_2017.dat.gz")

cps_2017_ddi <- read_ipums_ddi(cps_2017_ddi_file)
cps_2017_data <- read_ipums_micro(cps_2017_ddi_file, data_file = cps_2017_data_file, verbose = FALSE)

# Volunteering data from September 2015 only
df_volunteering <- cps_2015_data %>% 
  # Remove values not in the universe
  mutate_at(vars(VLSTATUS, VLDONATE), funs(ifelse(. == 99, NA, .)))

# All other data from annual March 2017 survey
df_demographics <- cps_2017_data %>% 
  # Remove values not in the universe
  mutate(SEX = ifelse(SEX == 9, NA, SEX),
         EDUC = ifelse(EDUC <= 1 | EDUC == 999, NA, EDUC),
         INCTOT = ifelse(INCTOT == 99999999, NA, INCTOT))
```

```{r population-values}
global_demographics <- df_demographics %>% 
  summarize(age = weighted.mean(AGE >= 35, ASECWT), 
            female = weighted.mean(SEX == 2, ASECWT),
            college = weighted.mean(EDUC >= 111, ASECWT, na.rm = TRUE),
            income = weighted.mean(INCTOT >= 50000, ASECWT, na.rm = TRUE)) %>% 
  c()

global_vol <- df_volunteering %>% 
  summarize(volunteering = weighted.mean(VLSTATUS == 2, VLSUPPWT, na.rm = TRUE),
            donating = weighted.mean(VLDONATE == 2, VLSUPPWT, na.rm = TRUE)) %>% 
  c()

global_stats <- c(global_vol, global_demographics)
```

```{r sample-population-characteristics, cache=TRUE}
compare_sample_to_pop <- function(sample_value, population_value) {
  mcmc_samples <- pop_prop_stan(
    x = table(sample_value)[1],
    n_total = length(sample_value),
    pop_prop = population_value,
    chains = CHAINS, iter = ITER, warmup = WARMUP, seed = BAYES_SEED)

  tidied <- tidyMCMC(mcmc_samples, conf.int = TRUE, conf.level = 0.95, 
                     estimate.method = "median", conf.method = "HPDinterval") %>%
    mutate(in_hpdi = (population_value >= conf.low & population_value <= conf.high))
  
  thetas <- unlist(extract(mcmc_samples, "theta"))
  pop_quantile_in_sample <- ecdf(thetas)(population_value)
  
  in_hpdi <- (population_value >= tidied[1,]$conf.low & 
                population_value <= tidied[1,]$conf.high)
  
  return(list(mcmc_samples = mcmc_samples, tidied = tidied, theta_in_hpdi = in_hpdi,
              pop_quantile_in_sample = pop_quantile_in_sample))
}

calc_sample_pop <- tribble(
  ~Variable, ~sample_value, ~National,
  "Female (%)^a^", results$gender_bin, global_stats$female,
  "Age (% 35+)^a^", results$age_bin, global_stats$age,
  "Income (% $50,000+)^a^", results$income_bin, global_stats$income,
  "Education (% BA+)^a^", results$education_bin, global_stats$college,
  "Donated in past year (%)^b^", results$give_charity_2, global_stats$donating,
  "Volunteered in past year (%)^b^", results$volunteer, global_stats$volunteering
) %>% 
  mutate(Sample = sample_value %>% map_dbl(~ prop.table(table(.))[1]),
         prop_test_bayes = map2(.x = sample_value, .y = National, 
                                .f = ~ compare_sample_to_pop(.x, .y))) 
```

```{r tbl-sample-characteristics, results="asis"}
format_hpdi <- function(post_lower, post_upper, star, digits = 1) {
  glue("({lower}%, {upper}%){star}",
       lower = round(100 * post_lower, digits),
       upper = round(100 * post_upper, digits))
}

tbl_sample_pop <- calc_sample_pop %>% 
  mutate(in_hpdi = prop_test_bayes %>% map_lgl(~ .$theta_in_hpdi),
         not_hpdi_symbol = ifelse(in_hpdi, "", "^†^"),
         diffs_tidy = prop_test_bayes %>% map(~ .$tidied[2,]),
         diffs_median = diffs_tidy %>% map_dbl(~ .$estimate),
         diffs_hpdi_fancy = diffs_tidy %>%
           map2_chr(.x = diffs_tidy, .y = not_hpdi_symbol, 
                    .f = ~ format_hpdi(.x$conf.low, .x$conf.high, .y))) %>% 
  mutate_at(vars(National, Sample, diffs_median), funs(percent)) %>% 
  select(Variable, Sample, National, 
         `∆~median~` = diffs_median,
         `95% HPDI` = diffs_hpdi_fancy)

note_row <- tibble(Variable = c("*^a^Annual CPS, March 2017*",
                                "*^b^Monthly CPS, September 2015*",
                                "*^†^National value is outside the sample highest posterior density interval (HPDI)*"))

bind_rows(tbl_sample_pop, note_row) %>% 
  pandoc.table.return(keep.line.breaks = TRUE, style = "multiline", justify = "lcccc", 
                      caption = "Characteristics of experimental sample {#tbl:exp-sample}") %T>% 
  cat(file = here("analysis", "output", "tables", "tbl-exp-sample.md")) %>% 
  cat()
```


# Miscellaneous survey details

## Average time to complete survey

```{r avg-time, results="asis"}
time_summary <- results %>% 
  summarize_at(vars(duration), funs(Minimum = min, Maximum = max, Mean = mean, 
                                    `Standard deviation` = sd, Median = median)) %>% 
  gather(Statistic, value) %>% 
  mutate(Minutes = fmt_seconds(value)) %>% 
  select(-value) 

pandoc.table(time_summary)
```

```{r avg-time-plot, fig.height=2.5, fig.width=4}
summary_stats <- tableGrob(time_summary, rows = NULL, theme = theme_ngos_table) %>% 
  gtable::gtable_add_grob(., grobs = rectGrob(gp = gpar(fill = NA, lwd = 1)),
                          t = 1, b = nrow(.), l = 1, r = ncol(.))

plot_avg_time <- ggplot(results, aes(x = duration)) +
  geom_histogram(bins = 50, fill = ngo_cols("blue")) +
  scale_x_time(labels = fmt_seconds) +
  annotation_custom(summary_stats, xmin = 700, xmax = 900, ymin = 30, ymax = 60) +
  labs(x = "Minutes spent on experiment", y = "Count") +
  theme_ngos(base_size = 9.5) +
  theme(panel.grid.minor = element_blank())

plot_avg_time %T>% 
  print() %T>%
  ggsave(., filename = here("analysis", "output", "figures", "avg-time.pdf"),
         width = 4, height = 2.25, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("analysis", "output", "figures", "avg-time.png"),
         width = 4, height = 2.25, units = "in", type = "cairo", dpi = 300)
```


# Original computing environment

<button data-toggle="collapse" data-target="#sessioninfo" class="btn btn-primary btn-md btn-info">Here's what we used the last time we built this page</button>

<div id="sessioninfo" class="collapse">

```{r show-session-info, echo=TRUE, width=100}
writeLines(readLines(file.path(Sys.getenv("HOME"), ".R/Makevars")))

devtools::session_info()
```

</div> 
